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首页> 外文期刊>BioTechnology: An Indian Journal >Prognosis and Diagnosis of Breast Cancer Using Interactive Dashboard Through Big Data Analytics
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Prognosis and Diagnosis of Breast Cancer Using Interactive Dashboard Through Big Data Analytics

机译：通过大数据分析使用交互式仪表板对乳腺癌的预后和诊断

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Background: Cancer is a life threatening disease of present scenario among which breast cancer is the second highly mortal disease in women. There are several stages of cancer and an early detection of breast cancer can reduce the mortality rate. The primary detection of breast cancer is mammography due to the naked eye prediction of the disease by radiologist, they suggest for the next level of diagnosis like MRI, PET or biopsy. These tests are time consuming and not cost effective. In this work, we aim for an interactive dashboard methodology to determine the presence or absence of a distinct mass called tumor in mammographic image. Here we also tend to confirm the mass to be benign or malignant by analyzing the shape of the mass using image processing techniques. We also predict a possibility to determine the stages of breast cancer using big data and cloud as a next level to the computer aided method of cancer detection. Methodology: In this paper, we also use Backpropagation and Support vector machine system (SVM) for analysis of benign or malignant cancer, we can also predict the stage of cancer using mammographic images. Conclusion: The results performed in nntraintool determine the performance rate, training state, regression and error histogram of the test image. And the cloud and big data analysis idea suggest to a next level of home level cancer stage prediction.
机译：背景：癌症是当前威胁生命的疾病，其中乳腺癌是女性中第二致命的疾病。癌症分为几个阶段，尽早发现乳腺癌可以降低死亡率。乳腺癌的主要检测方法是放射线照相术，这是因为放射科医生会用肉眼预测该疾病，因此他们建议对MRI，PET或活检进行进一步的诊断。这些测试既费时又不经济。在这项工作中，我们的目标是采用交互式仪表板方法来确定乳房X线照片中是否存在称为肿瘤的独特肿块。在这里，我们还倾向于通过使用图像处理技术分析肿块的形状来确定肿块是良性还是恶性的。我们还预测了使用大数据和云作为确定癌症检测计算机辅助方法的下一个层次来确定乳腺癌分期的可能性。方法：在本文中，我们还使用反向传播和支持向量机系统（SVM）来分析良性或恶性癌症，我们还可以使用乳腺X线照片来预测癌症的分期。结论：在nntraintool中执行的结果确定了测试图像的性能，训练状态，回归和误差直方图。而云计算和大数据分析的想法则建议对家庭癌症阶段进行下一阶段的预测。

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